If orthogonal contrasts are available, it is possible to summarize the results of a statistical analysis in the form of a simple analysis of variance table, in such a way that it contains the results for different test statistics relating to different contrasts, each of which are statistically independent. Linear contrasts can be easily converted into sums of squares . SS contrast =
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{\displaystyle {\tfrac {n(\sum c_{j}{\bar {X}}_{j})^{2}}{\sum c_{j}^{2}}}}
, with 1 degree of freedom , where * n* represents the number of observations per group. If the contrasts are orthogonal, the sum of the SS contrasts = SS treatment . Testing the significance of a contrast requires the computation of SS contrast . [8] A recent development in statistical analysis is the standardized mean of a contrast variable . This makes a comparison between the size of the differences between groups, as measured by a contrast and the accuracy with which that contrast can be measured by a given study or experiment. [13]

The above examples of comparison help us realize that in general, writers utilize different kinds of comparisons to link an unfamiliar or a new idea to common and familiar objects. It facilitates readers to comprehend a new idea, which may have been difficult for them to understand otherwise. The understanding of a new idea turns out to be simpler when viewed with a comparison to something that is familiar to them. In addition, by making use of various literary tools for comparison, writers increase their chance of catching the attention and interest of their readers, as comparisons help them identify what they are reading to their lives.